How to pivot a dataframe? | Python Pandas [Answered]

Problem:

  • What is pivot?
  • How do I pivot?
  • Is this a pivot?
  • Long format to wide format?

I’ve seen a lot of questions that ask about pivot tables. Even if they don’t know that they are asking about pivot tables, they usually are. It is virtually impossible to write a canonical question and answer that encompasses all aspects of pivoting…

… But I’m going to give it a go.


Setup

You may notice that I conspicuously named my columns and relevant column values to correspond with how I’m going to pivot in the answers below.

import numpy as np
import pandas as pd
from numpy.core.defchararray import add

np.random.seed([3,1415])
n = 20

cols = np.array(['key', 'row', 'item', 'col'])
arr1 = (np.random.randint(5, size=(n, 4)) // [2, 1, 2, 1]).astype(str)

df = pd.DataFrame(
    add(cols, arr1), columns=cols
).join(
    pd.DataFrame(np.random.rand(n, 2).round(2)).add_prefix('val')
)
print(df)

     key   row   item   col  val0  val1
0   key0  row3  item1  col3  0.81  0.04
1   key1  row2  item1  col2  0.44  0.07
2   key1  row0  item1  col0  0.77  0.01
3   key0  row4  item0  col2  0.15  0.59
4   key1  row0  item2  col1  0.81  0.64
5   key1  row2  item2  col4  0.13  0.88
6   key2  row4  item1  col3  0.88  0.39
7   key1  row4  item1  col1  0.10  0.07
8   key1  row0  item2  col4  0.65  0.02
9   key1  row2  item0  col2  0.35  0.61
10  key2  row0  item2  col1  0.40  0.85
11  key2  row4  item1  col2  0.64  0.25
12  key0  row2  item2  col3  0.50  0.44
13  key0  row4  item1  col4  0.24  0.46
14  key1  row3  item2  col3  0.28  0.11
15  key0  row3  item1  col1  0.31  0.23
16  key0  row0  item2  col3  0.86  0.01
17  key0  row4  item0  col3  0.64  0.21
18  key2  row2  item2  col0  0.13  0.45
19  key0  row2  item0  col4  0.37  0.70

Question(s)

  1. Why do I get ValueError: Index contains duplicate entries, cannot reshape
  2. How do I pivot df such that the col values are columns, row values are the index, and mean of val0 are the values? col col0 col1 col2 col3 col4 row row0 0.77 0.605 NaN 0.860 0.65 row2 0.13 NaN 0.395 0.500 0.25 row3 NaN 0.310 NaN 0.545 NaN row4 NaN 0.100 0.395 0.760 0.24
  3. How do I pivot df such that the col values are columns, row values are the index, mean of val0 are the values, and missing values are 0? col col0 col1 col2 col3 col4 row row0 0.77 0.605 0.000 0.860 0.65 row2 0.13 0.000 0.395 0.500 0.25 row3 0.00 0.310 0.000 0.545 0.00 row4 0.00 0.100 0.395 0.760 0.24
  4. Can I get something other than mean, like maybe sum? col col0 col1 col2 col3 col4 row row0 0.77 1.21 0.00 0.86 0.65 row2 0.13 0.00 0.79 0.50 0.50 row3 0.00 0.31 0.00 1.09 0.00 row4 0.00 0.10 0.79 1.52 0.24
  5. Can I do more that one aggregation at a time? sum mean col col0 col1 col2 col3 col4 col0 col1 col2 col3 col4 row row0 0.77 1.21 0.00 0.86 0.65 0.77 0.605 0.000 0.860 0.65 row2 0.13 0.00 0.79 0.50 0.50 0.13 0.000 0.395 0.500 0.25 row3 0.00 0.31 0.00 1.09 0.00 0.00 0.310 0.000 0.545 0.00 row4 0.00 0.10 0.79 1.52 0.24 0.00 0.100 0.395 0.760 0.24
  6. Can I aggregate over multiple value columns? val0 val1 col col0 col1 col2 col3 col4 col0 col1 col2 col3 col4 row row0 0.77 0.605 0.000 0.860 0.65 0.01 0.745 0.00 0.010 0.02 row2 0.13 0.000 0.395 0.500 0.25 0.45 0.000 0.34 0.440 0.79 row3 0.00 0.310 0.000 0.545 0.00 0.00 0.230 0.00 0.075 0.00 row4 0.00 0.100 0.395 0.760 0.24 0.00 0.070 0.42 0.300 0.46
  7. Can Subdivide by multiple columns? item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 row row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.605 0.86 0.65 row2 0.35 0.00 0.37 0.00 0.00 0.44 0.00 0.00 0.13 0.000 0.50 0.13 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.000 0.28 0.00 row4 0.15 0.64 0.00 0.00 0.10 0.64 0.88 0.24 0.00 0.000 0.00 0.00
  8. Or item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 key row key0 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.86 0.00 row2 0.00 0.00 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.00 0.00 0.00 row4 0.15 0.64 0.00 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 key1 row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.81 0.00 0.65 row2 0.35 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.13 row3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 row4 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 key2 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.00 0.00 row2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 row4 0.00 0.00 0.00 0.00 0.00 0.64 0.88 0.00 0.00 0.00 0.00 0.00
  9. Can I aggregate the frequency in which the column and rows occur together, aka “cross tabulation”? col col0 col1 col2 col3 col4 row row0 1 2 0 1 1 row2 1 0 2 1 2 row3 0 1 0 2 0 row4 0 1 2 2 1
  10. How do I convert a DataFrame from long to wide by pivoting on ONLY two columns? Given,np.random.seed([3, 1415]) df2 = pd.DataFrame({'A': list('aaaabbbc'), 'B': np.random.choice(15, 8)}) df2 A B 0 a 0 1 a 11 2 a 2 3 a 11 4 b 10 5 b 10 6 b 14 7 c 7 The expected should look something like a b c 0 0.0 10.0 7.0 1 11.0 10.0 NaN 2 2.0 14.0 NaN 3 11.0 NaN NaN
  11. How do I flatten the multiple index to single index after pivot?From 1 2 1 1 2 a 2 1 1 b 2 1 0 c 1 0 0 To 1|1 2|1 2|2 a 2 1 1 b 2 1 0 c 1 0 0

How to pivot a dataframe?Answer #1:

We start by answering the first question:

Question 1

Why do I get ValueError: Index contains duplicate entries, cannot reshape

This occurs because pandas is attempting to reindex either a columns or index object with duplicate entries. There are varying methods to use that can perform a pivot. Some of them are not well suited to when there are duplicates of the keys in which it is being asked to pivot on. For example. Consider pd.DataFrame.pivot. I know there are duplicate entries that share the row and col values:

df.duplicated(['row', 'col']).any()

True

So when I pivot using

df.pivot(index='row', columns='col', values='val0')

I get the error mentioned above. In fact, I get the same error when I try to perform the same task with:

df.set_index(['row', 'col'])['val0'].unstack()

Here is a list of idioms we can use to pivot

  1. pd.DataFrame.groupby + pd.DataFrame.unstack
    • Good general approach for doing just about any type of pivot
    • You specify all columns that will constitute the pivoted row levels and column levels in one group by. You follow that by selecting the remaining columns you want to aggregate and the function(s) you want to perform the aggregation. Finally, you unstack the levels that you want to be in the column index.
  2. pd.DataFrame.pivot_table
    • A glorified version of groupby with more intuitive API. For many people, this is the preferred approach. And is the intended approach by the developers.
    • Specify row level, column levels, values to be aggregated, and function(s) to perform aggregations.
  3. pd.DataFrame.set_index + pd.DataFrame.unstack
    • Convenient and intuitive for some (myself included). Cannot handle duplicate grouped keys.
    • Similar to the groupby paradigm, we specify all columns that will eventually be either row or column levels and set those to be the index. We then unstack the levels we want in the columns. If either the remaining index levels or column levels are not unique, this method will fail.
  4. pd.DataFrame.pivot
    • Very similar to set_index in that it shares the duplicate key limitation. The API is very limited as well. It only takes scalar values for indexcolumnsvalues.
    • Similar to the pivot_table method in that we select rows, columns, and values on which to pivot. However, we cannot aggregate and if either rows or columns are not unique, this method will fail.
  5. pd.crosstab
    • This a specialized version of pivot_table and in its purest form is the most intuitive way to perform several tasks.
  6. pd.factorize + np.bincount
    • This is a highly advanced technique that is very obscure but is very fast. It cannot be used in all circumstances, but when it can be used and you are comfortable using it, you will reap the performance rewards.
  7. pd.get_dummies + pd.DataFrame.dot
    • I use this for cleverly performing cross tabulation.

Examples

What I’m going to do for each subsequent answer and question is to answer it using pd.DataFrame.pivot_table. Then I’ll provide alternatives to perform the same task.

Question 3

How do I pivot df such that the col values are columns, row values are the index, mean of val0 are the values, and missing values are 0?

  • pd.DataFrame.pivot_table
    • fill_value is not set by default. I tend to set it appropriately. In this case I set it to 0. Notice I skipped question 2 as it’s the same as this answer without the fill_value
    • aggfunc='mean' is the default and I didn’t have to set it. I included it to be explicit. df.pivot_table( values='val0', index='row', columns='col', fill_value=0, aggfunc='mean') col col0 col1 col2 col3 col4 row row0 0.77 0.605 0.000 0.860 0.65 row2 0.13 0.000 0.395 0.500 0.25 row3 0.00 0.310 0.000 0.545 0.00 row4 0.00 0.100 0.395 0.760 0.24
  • pd.DataFrame.groupby df.groupby(['row', 'col'])['val0'].mean().unstack(fill_value=0)
  • pd.crosstab pd.crosstab( index=df['row'], columns=df['col'], values=df['val0'], aggfunc='mean').fillna(0)

Question 4

Can I get something other than mean, like maybe sum?

  • pd.DataFrame.pivot_table df.pivot_table( values='val0', index='row', columns='col', fill_value=0, aggfunc='sum') col col0 col1 col2 col3 col4 row row0 0.77 1.21 0.00 0.86 0.65 row2 0.13 0.00 0.79 0.50 0.50 row3 0.00 0.31 0.00 1.09 0.00 row4 0.00 0.10 0.79 1.52 0.24
  • pd.DataFrame.groupby df.groupby(['row', 'col'])['val0'].sum().unstack(fill_value=0)
  • pd.crosstab pd.crosstab( index=df['row'], columns=df['col'], values=df['val0'], aggfunc='sum').fillna(0)

Question 5

Can I do more that one aggregation at a time?

Notice that for pivot_table and crosstab I needed to pass list of callables. On the other hand, groupby.agg is able to take strings for a limited number of special functions. groupby.agg would also have taken the same callables we passed to the others, but it is often more efficient to leverage the string function names as there are efficiencies to be gained.

  • pd.DataFrame.pivot_table df.pivot_table( values='val0', index='row', columns='col', fill_value=0, aggfunc=[np.size, np.mean]) size mean col col0 col1 col2 col3 col4 col0 col1 col2 col3 col4 row row0 1 2 0 1 1 0.77 0.605 0.000 0.860 0.65 row2 1 0 2 1 2 0.13 0.000 0.395 0.500 0.25 row3 0 1 0 2 0 0.00 0.310 0.000 0.545 0.00 row4 0 1 2 2 1 0.00 0.100 0.395 0.760 0.24
  • pd.DataFrame.groupby df.groupby(['row', 'col'])['val0'].agg(['size', 'mean']).unstack(fill_value=0)
  • pd.crosstab pd.crosstab( index=df['row'], columns=df['col'], values=df['val0'], aggfunc=[np.size, np.mean]).fillna(0, downcast='infer')

Question 6

Can I aggregate over multiple value columns?

  • pd.DataFrame.pivot_table we pass values=['val0', 'val1'] but we could’ve left that off completely df.pivot_table( values=['val0', 'val1'], index='row', columns='col', fill_value=0, aggfunc='mean') val0 val1 col col0 col1 col2 col3 col4 col0 col1 col2 col3 col4 row row0 0.77 0.605 0.000 0.860 0.65 0.01 0.745 0.00 0.010 0.02 row2 0.13 0.000 0.395 0.500 0.25 0.45 0.000 0.34 0.440 0.79 row3 0.00 0.310 0.000 0.545 0.00 0.00 0.230 0.00 0.075 0.00 row4 0.00 0.100 0.395 0.760 0.24 0.00 0.070 0.42 0.300 0.46
  • pd.DataFrame.groupby df.groupby(['row', 'col'])['val0', 'val1'].mean().unstack(fill_value=0)

Question 7

Can Subdivide by multiple columns?

  • pd.DataFrame.pivot_table df.pivot_table( values='val0', index='row', columns=['item', 'col'], fill_value=0, aggfunc='mean') item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 row row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.605 0.86 0.65 row2 0.35 0.00 0.37 0.00 0.00 0.44 0.00 0.00 0.13 0.000 0.50 0.13 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.000 0.28 0.00 row4 0.15 0.64 0.00 0.00 0.10 0.64 0.88 0.24 0.00 0.000 0.00 0.00
  • pd.DataFrame.groupby df.groupby( ['row', 'item', 'col'] )['val0'].mean().unstack(['item', 'col']).fillna(0).sort_index(1)

Question 8

Can Subdivide by multiple columns?

  • pd.DataFrame.pivot_table df.pivot_table( values='val0', index=['key', 'row'], columns=['item', 'col'], fill_value=0, aggfunc='mean') item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 key row key0 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.86 0.00 row2 0.00 0.00 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.00 0.00 0.00 row4 0.15 0.64 0.00 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 key1 row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.81 0.00 0.65 row2 0.35 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.13 row3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 row4 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 key2 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.00 0.00 row2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 row4 0.00 0.00 0.00 0.00 0.00 0.64 0.88 0.00 0.00 0.00 0.00 0.00
  • pd.DataFrame.groupby df.groupby( ['key', 'row', 'item', 'col'] )['val0'].mean().unstack(['item', 'col']).fillna(0).sort_index(1)
  • pd.DataFrame.set_index because the set of keys are unique for both rows and columns df.set_index( ['key', 'row', 'item', 'col'] )['val0'].unstack(['item', 'col']).fillna(0).sort_index(1)

Question 9

Can I aggregate the frequency in which the column and rows occur together, aka “cross tabulation”?

  • pd.DataFrame.pivot_table df.pivot_table(index='row', columns='col', fill_value=0, aggfunc='size') col col0 col1 col2 col3 col4 row row0 1 2 0 1 1 row2 1 0 2 1 2 row3 0 1 0 2 0 row4 0 1 2 2 1
  • pd.DataFrame.groupby df.groupby(['row', 'col'])['val0'].size().unstack(fill_value=0)
  • pd.crosstab pd.crosstab(df['row'], df['col'])
  • pd.factorize + np.bincount # get integer factorization `i` and unique values `r` # for column `'row'` i, r = pd.factorize(df['row'].values) # get integer factorization `j` and unique values `c` # for column `'col'` j, c = pd.factorize(df['col'].values) # `n` will be the number of rows # `m` will be the number of columns n, m = r.size, c.size # `i * m + j` is a clever way of counting the # factorization bins assuming a flat array of length # `n * m`. Which is why we subsequently reshape as `(n, m)` b = np.bincount(i * m + j, minlength=n * m).reshape(n, m) # BTW, whenever I read this, I think 'Bean, Rice, and Cheese' pd.DataFrame(b, r, c) col3 col2 col0 col1 col4 row3 2 0 0 1 0 row2 1 2 1 0 2 row0 1 0 1 2 1 row4 2 2 0 1 1
  • pd.get_dummies pd.get_dummies(df['row']).T.dot(pd.get_dummies(df['col'])) col0 col1 col2 col3 col4 row0 1 2 0 1 1 row2 1 0 2 1 2 row3 0 1 0 2 0 row4 0 1 2 2 1

Question 10

How do I convert a DataFrame from long to wide by pivoting on ONLY two columns?

  • DataFrame.pivotThe first step is to assign a number to each row – this number will be the row index of that value in the pivoted result. This is done using GroupBy.cumcount: df2.insert(0, 'count', df2.groupby('A').cumcount()) df2 count A B 0 0 a 0 1 1 a 11 2 2 a 2 3 3 a 11 4 0 b 10 5 1 b 10 6 2 b 14 7 0 c 7 The second step is to use the newly created column as the index to call DataFrame.pivot. df2.pivot(*df2) # df2.pivot(index='count', columns='A', values='B') A a b c count 0 0.0 10.0 7.0 1 11.0 10.0 NaN 2 2.0 14.0 NaN 3 11.0 NaN NaN
  • DataFrame.pivot_tableWhereas DataFrame.pivot only accepts columns, DataFrame.pivot_table also accepts arrays, so the GroupBy.cumcount can be passed directly as the index without creating an explicit column. df2.pivot_table(index=df2.groupby('A').cumcount(), columns='A', values='B') A a b c 0 0.0 10.0 7.0 1 11.0 10.0 NaN 2 2.0 14.0 NaN 3 11.0 NaN NaN

Question 10.1

DataFrame:

d = data = {'A': {0: 1, 1: 1, 2: 1, 3: 2, 4: 2, 5: 3, 6: 5},
 'B': {0: 'a', 1: 'b', 2: 'c', 3: 'a', 4: 'b', 5: 'a', 6: 'c'}}
df = pd.DataFrame(d)

   A  B
0  1  a
1  1  b
2  1  c
3  2  a
4  2  b
5  3  a
6  5  c

Output:

   0     1     2
A
1  a     b     c
2  a     b  None
3  a  None  None
5  c  None  None

Using df.groupby and pd.Series.tolist

t = df.groupby('A')['B'].apply(list)
out = pd.DataFrame(t.tolist(),index=t.index)
out
   0     1     2
A
1  a     b     c
2  a     b  None
3  a  None  None
5  c  None  None

Or A much better alternative using pd.pivot_table with df.squeeze.

t = df.pivot_table(index='A',values='B',aggfunc=list).squeeze()
out = pd.DataFrame(t.tolist(),index=t.index)

Question 11

How do I flatten the multiple index to single index after pivot

If columns type object with string join

df.columns = df.columns.map('|'.join)

else format

df.columns = df.columns.map('{0[0]}|{0[1]}'.format)

How can I pivot a dataframe? Answer #2:

To better understand how pivot works you can look at the example from Pandas documentation:

enter image description here
df = pd.DataFrame({
    'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
    'baz': [1, 2, 3, 4, 5, 6],
    'zoo': ['x', 'y', 'z', 'q', 'w', 't']
})

Input Table:

   foo bar  baz zoo
0  one   A    1   x
1  one   B    2   y
2  one   C    3   z
3  two   A    4   q
4  two   B    5   w
5  two   C    6   t

Pivot:

pd.pivot(
    data=df,        
    index='foo',    # Column to use to make new frame’s index. If None, uses existing index.
    columns='bar',  # Column to use to make new frame’s columns.
    values='baz'    # Column(s) to use for populating new frame’s values.
)

Output table:

bar  A  B  C
foo         
one  1  2  3
two  4  5  6

About ᴾᴿᴼᵍʳᵃᵐᵐᵉʳ

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